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Generate relevant synthetic data quickly for your projects. The Databricks Labs synthetic data generator (aka `dbldatagen`) may be used to generate large simulated / synthetic data sets for test, POCs, and other uses in Databricks environments including in Delta Live Tables pipelines
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| 2026-07-24 | 484 |
| 2026-07-25 | 485 |
| 2026-07-28 | 485 |
| 2026-07-30 | 485 |
| 2026-08-06 | 485 |
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# Databricks Labs Data Generator (`dbldatagen`) <!-- Top bar will be removed from PyPi packaged versions --> <!-- Dont remove: exclude package --> [Documentation](https://databrickslabs.github.io/dbldatagen/public_docs/index.html) | [Release Notes](CHANGELOG.md) | [Examples](examples) | [Tutorial](tutorial) <!-- Dont remove: end exclude package --> [](https://github.com/databrickslabs/dbldatagen/actions?query=workflow%3Abuild+branch%3Amaster) [](https://pypi.org/project/dbldatagen/) [](https://codecov.io/gh/databrickslabs/dbldatagen) [](https://pypistats.org/packages/dbldatagen) []([https://codecov.io/github/databrickslabs/dbldatagen](https://github.com/databrickslabs/dbldatagen)) <!-- [](https://lgtm.com/projects/g/databrickslabs/dbldatagen/context:python) [](https://hanadigital.github.io/grev/?user=databrickslabs&repo=dbldatagen) --> ## Project Description The `dbldatagen` Databricks Labs project is a Python library for generating synthetic data within the Databricks environment using Spark. The generated data may be used for testing, benchmarking, demos, and many other uses. It operates by defining a data generation specification in code that controls how the synthetic data is generated. The specification may incorporate the use of existing schemas or create data in an ad-hoc fashion. It has no dependencies on any libraries that are not already installed in the Databricks runtime, and you can use it from Scala, R or other languages by defining a view over the generated data. ### Feature Summary It supports: * Generating synthetic data at scale up to billions of rows within minutes using appropriately sized clusters * Generating repeatable, predictable data supporting the need for producing multiple tables, Change Data Capture, merge and join scenarios with consistency between primary and foreign keys * Generating synthetic data for all of the Spark SQL supported primitive types as a Spark data frame which may be persisted, saved to external storage or used in other computations * Generating ranges of dates, timestamps, and numeric values * Generation of discrete values - both numeric and text * Generation of values at random and based on the values of other fields (either based on the `hash` of the underlying values or the values themselves) * Ability to specify a distribution for random data generation * Generating arrays of values for ML-style feature arrays * Applying weights to the occurrence of values * Generating values to conform to a schema or independent of an existing schema * use of SQL expressions in synthetic data generation * plugin mechanism to allow use of 3rd party libraries such as Faker * Use within a Databricks Delta Live Tables pipeline as a synthetic data generation source * Generate synthetic data generation code from existing schema or data (experimental) * Use of standard datasets for quick generation of synthetic data Details of these features can be found in the online documentation - [online documentation](https://databrickslabs.github.io/dbldatagen/public_docs/index.html). ## Documentation Please refer to the [online documentation](https://databrickslabs.github.io/dbldatagen/public_docs/index.html) for details of use and many examples. Release notes and details of the latest changes for this specific release can be found in the GitHub repository [here](https://gi
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matched fp:c2e156e565ec712a, topic:synthetic-data, desc:synthetic data, readme:synthetic data